Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds

TL;DR

Point2SSM++ is a self-supervised deep learning framework that efficiently learns anatomical shape models directly from point clouds, overcoming previous limitations of alignment and inference speed, and broadening clinical applications.

Contribution

It introduces a novel self-supervised method for correspondence learning in shape modeling from point clouds, with extensions for dynamic and multi-anatomy scenarios, outperforming existing methods.

Findings

Outperforms state-of-the-art models in shape correspondence accuracy.

Robust to misaligned and inconsistent input data.

Enables efficient and versatile shape modeling for clinical use.

Abstract

Correspondence-based statistical shape modeling (SSM) stands as a powerful technology for morphometric analysis in clinical research. SSM facilitates population-level characterization and quantification of anatomical shapes such as bones and organs, aiding in pathology and disease diagnostics and treatment planning. Despite its potential, SSM remains under-utilized in medical research due to the significant overhead associated with automatic construction methods, which demand complete, aligned shape surface representations. Additionally, optimization-based techniques rely on bias-inducing assumptions or templates and have prolonged inference times as the entire cohort is simultaneously optimized. To overcome these challenges, we introduce Point2SSM++, a principled, self-supervised deep learning approach that directly learns correspondence points from point cloud representations of anatomical shapes. Point2SSM++ is robust to misaligned and inconsistent input, providing SSM that accurately samples individual shape surfaces while effectively capturing population-level statistics. Additionally, we present principled extensions of Point2SSM++ to adapt it for dynamic spatiotemporal and multi-anatomy use cases, demonstrating the broad versatility of the Point2SSM++ framework. Furthermore, we present extensions of Point2SSM++ tailored for dynamic spatiotemporal and multi-anatomy scenarios, showcasing the broad versatility of the framework. Through extensive validation across diverse anatomies, evaluation metrics, and clinically relevant downstream tasks, we demonstrate Point2SSM++'s superiority over existing state-of-the-art deep learning models and traditional approaches. Point2SSM++ substantially enhances the feasibility of SSM generation and significantly broadens its array of potential clinical applications.